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Homology
by Bob Friedman
bird wing
bat wing
human arm
homology vs analogy
A priori sequences could be similar due to convergent evolution
Homology (shared ancestry) versus Analogy (convergent evolution)
bird wing
bat wing
butterfly wing
fly wing
Two components of similarity searching
Searching method - a model of sequence evolution
by Bob Friedman
Database - search only as good as sequences searching against
by Bob Friedman
Types of Blast searching
•
blastp compares an amino acid query sequence against a
protein sequence database
•
blastn compares a nucleotide query sequence against a
nucleotide sequence database
•
blastx compares the six-frame conceptual protein translation
products of a nucleotide query sequence against a protein
sequence database
•
tblastn compares a protein query sequence against a
nucleotide sequence database translated in six reading frames
•
tblastx compares the six-frame translations of a nucleotide
query sequence against the six-frame translations of a
nucleotide sequence database.
Routine BlastP search
FASTA formatted
text
or Genbank ID#
Protein
database
by Bob Friedman
Run
BlastP parameters
Restrict by taxonomic
group
Filter repetitive regions
Statistical cut-off
Size of words in
look-up table
by Bob Friedman
Similarity matrix
(cost of gaps)
Establishing a significant “hit”
Blast’s E-value indicates statistical significance of a sequence match
Karlin S, Altschul SF (1990) Methods for assessing the statistical significance of molecular
sequence features by using general scoring schemes. PNAS 87:2264-8
E-value is the Expected number of sequence (HSPs) matches in database of
n number of sequences
• database size is arbitrary
• multiple testing problem
• E-value calculated from many assumptions
• so, E-value is not easily compared between searches of different databases
by Bob Friedman
Examples:
E-value = 1 = expect the match to occur in the database by chance 1x
E-value = .05 = expect 5% chance of match occurring
E-value = 1x10-20 = strict match between protein domains
When are two sequences significantly similar? PRSS
One way to quantify the similarity between two sequences is to
1. compare the actual sequences and calculate an alignment score
2. randomize (scramble) one (or both) of the sequences and
calculate the alignment score for the randomized sequences.
3. repeat step 2 at least 100 times
4. describe distribution of randomized alignment scores
5. do a statistical test to determine if the score obtained for the real
sequences is significantly better than the score for the randomized
sequences
z-values give the distance between the actual alignment score and the mean of
the scores for the randomized sequences expressed as multiples of the standard
deviation calculated for the randomized scores.
For example: a z-value of 3 means that the actual alignment score is 3 standard
deviations better than the average for the randomized sequences. z-values > 3
are usually considered as suggestive of homology, z-values > 5 are considered as
sufficient demonstration.
PRSS continued
To illustrate the assessment of similarity/homology we will use a program
from Pearson's FASTA package called PRSS.
This and many other programs by Bill Pearson are available from his web
page at ftp://ftp.virginia.edu/pub/fasta/.
A web version is available here.
Sequences for an in class example are here (fl), here (B), here (A) and here
(A2)
BLAST offers a similar service for pairwise sequence comparison bl2seq,
however, the statistical evaluation is less straightforward.
To force the bl2seq program to report an alignment increase the E-value.
E-values and significance
Usually E values larger than 0.0001 are not considered as demonstration of
homology.
For small values the E value gives the probability to find a match of this
quality in a search of a databank of the same size by chance alone.
E-values give the expected number of matches with an
alignment score this good or better,
P-values give the probability of to find a match of this
quality or better.
P values are [0,1], E-values are [0,infinity).
For small values E=P
Problem: If you do 1000 blast searches, you expect one match due to chance
with a P-value of 0.0001
“One should” use a correction for multiple tests, like the Bonferroni
correction.
Blast databases
•
•
EST - Expression Sequence Tags; cDNA
GSS - Genome Survey Sequence; single-pass genomic
sequences
•
HTGS - unfinished High Throughput Genomic Sequences
•
•
•
•
chromosome - complete chromosomes, complete genomes,
contigs
NR - non-redundant DNA or amino acid sequence database
NT - NR database excluding EST, STS, GSS, HTGS
PDB - DNA or amino acid sequences accompanied by 3d
structures
STS - Sequence Tagged Sites; short genomic markers for
mapping
Swissprot - well-annotated amino-acid sequences
TaxDB - taxonomy information
WGS_xx - whole genome shotgun assemblies
•
Also, to obtain organism-specific sequence set:
•
•
•
by Bob Friedman
•
ftp://ftp.ncbi.nih.gov/genomes/
by Bob Friedman
More databases
by Bob Friedman
And more databases
Example of web based BLAST
program: BLASTP
sequence: vma1
gi:137464
BLink provides similar
information
Effect of low complexity filter
BUT the most common sequences are simple repeats
Custom databases
Custom databases can include private sequence data, non-redundant
gene sets based on genomic locations, merging of genetic data from
specific organisms
It’s also faster to search only the sequence data that is necessary
by Bob Friedman
Can search against sequences with custom names
Uses of Blast in bioinformatics
The Blast web tool at NCBI is limited:
• custom and multiple databases are not available
• tBlastN (gene prediction) not available
• “time-out” before long searches are completed
by Bob Friedman
What if researcher wants to use tBlastN to find all olfactory receptors in
the mosquito?
Answer: Use Blast from command-line
The command-line allows the user to run commands repeatedly
Formatting a custom database
Format sequence data into Fasta format
Example of Fasta format:
>sequence 1
AAATGCTTAAAAA
>sequence 2
AAATTGCTAAAAGA
by Bob Friedman
Convert Fasta to Blast format by using FormatDB program from
command-line:
formatdb -p F -o T -i name_of_fasta_file
(formatdb.log is a file where the results are logged from the formatting
operation)
by Bob Friedman
BlastP search of custom database
Psi-Blast: Detecting structural homologs
Psi-Blast was designed to detect homology for highly divergent amino acid
sequences
Psi = position-specific iterated
Psi-Blast is a good technique to find “potential candidate” genes
Example: Search for Olfactory Receptor genes in Mosquito genome
by Bob Friedman
Hill CA, Fox AN, Pitts RJ, Kent LB, Tan PL, Chrystal MA, Cravchik A, Collins FH,
Robertson HM, Zwiebel LJ (2002) G protein-coupled receptors in Anopheles gambiae.
Science 298:176-8
Psi-Blast Model
Model of Psi-Blast:
1. Use results of gapped BlastP query to construct a multiple sequence
alignment
2. Construct a position-specific scoring matrix from the alignment
3. Search database with alignment instead of query sequence
4. Add matches to alignment and repeat
by Bob Friedman
Similar to Blast, the E-value in Psi-Blast is important in establishing
matches
E-value defaults to 0.001 & Blosom62
Psi-Blast can use existing multiple alignment - particularly powerful when
the gene functions are known (prior knowledge) or use RPS-Blast
database
PSI BLAST scheme
by Bob Friedman
Position-specific Matrix
M Gribskov, A D McLachlan, and D Eisenberg (1987) Profile analysis:
detection of distantly related proteins. PNAS 84:4355-8.
Psi-Blast Results
Query: 55670331 (intein)
link to sequence here,
check BLink 
Blast Summary
Blast is a fast program to find similar DNA or amino acid sequences in a
database
NCBI web tool for finding sequence similarity:
http://www.ncbi.nlm.nih.gov/BLAST/
E-value is a statistic to measure the significance of a “match”
Psi-Blast is for finding matches among divergent sequences (positionspecific information)
WARNING: For the nth iteration of a PSI BLAST search, the E-value
gives the number of matches to the profile NOT to the initial query
sequence! The danger is that the profile was corrupted in an earlier
iteration.
Summary of command-line Blast
Repetitive homology searching by use of command-line & scripting
language
Another advantage is searching against custom DNA or protein
database(s)
by Bob Friedman
Blast results can be processed by text-processing language
Demo using putty to bbcxsrv.biotech.uconn.edu
- maybe
follow instructions of exercise one task 6 – these are the commands
formatdb -i p_abyssi.faa -o T -p T
blastall -i t_maritima.faa -d p_abyssi.faa -o
blast.out -p blastp -e 10 -m 8 -a2
./extract_lines.pl blast.out
sftp results
load into spreadsheet
sort data, do histogram …
the extract_lines.pl script is here (you can sftp it into your
account, you’ll need to chmod 755 extr*.pl afterwards)
Command Line
The favored operating system flavor in computational biology is
UNIX/LINUX.
The command line is similar to DOS.
Some of the frequently used commands are here
pwd
ls
ls –l
chmod
chmod a+x blastall.sh
chmod 755 *.sh
cd
cd ..
cd $HOME
passwd
ps
ps aux
rm
more
cat
vi (text editor)
ps
ps aux
ssh
sftp
For windows a good ssh program is putty.
UConn also has a site license for the ssh program from ssh.com